1. Field of the Invention
This invention relates to digital image processing and more particularly, to methods and apparatuses for recognizing and/or verifying objects in a digital image. Specifically, the invention relates to object recognition using operators which encode representative features of objects appearing in the digital image.
2. Description of the Related Art
Object recognition is an increasingly important area of computer vision which has a wide range of practical applications, such as, for example, image archiving, retrieval and organization, manufacturing, and security. In light of the need for improved industrial and national security, and also given the dramatically increasing popularity of digital photography, face recognition is becoming an important facet of object recognition. However, accurate face recognition is often difficult due to imaging conditions which can change due to external and internal factors. External factors include illumination conditions (e.g., back-lit versus front-lit, or overcast versus direct sunlight) and camera poses (e.g., frontal view versus side view). Internal factors include variations which may result directly from the passage of time (e.g., people aging) or changing object states (e.g., different facial expressions and accessories). In the field of pattern recognition, variations which imaged objects exhibit due to varying imaging conditions are typically referred to as intra-class variations.
The ability of an algorithm to recognize objects across intra-class variations determines its success in practical applications. Face recognition has traditionally been approached using 3-D model based techniques and feature-based methods. A feature common to face recognition systems is a similarity measure—where faces are considered similar if they belong to the same individual. The similarity measure can be used to verify that two face images belong to the same person, or to classify novel images by determining to which of the given faces the new example is most similar. However, designing a good similarity measure is difficult. Simple similarity measures such as those based on the Euclidean distance used directly in the image space do not typically work well because the image can be affected more by the intra-class variations than by inter-class variations. Therefore, a face recognition algorithm should be able to extract the image features that maximize the inter-class differences relative to the intra-class ones.
To make the best decision about the identity of a novel face example, an ideal system would have a representation of all the possible variations in appearance of each person's face—either as a model of the face and the environment, or as a large number of views of each face. If a large number of examples of each person are available in the gallery, then a model of each person can be computed and used to classify novel views of faces. However, in practice, the gallery may contain only a few examples of each person.